Publications associées à Mort subite et inexpliquée en épilepsie

Hardware Implementation of Digital Signal Processing Algorithms for Programmable Epilepsy Control Systems

Epilepsy is a common chronic neurological disorder that causes recurring seizures and affects more than 50 million people worldwide. Implantable medical devices (IMDs) are regarded as effective tools to cure patients who suffer from refractory epilepsy. Se ...

EPFL2023

Real-time Personalized Monitoring of Neurological Disorders on Wearable Systems

Renato Zanetti

The terminology of neurological disorders encompasses a range of serious illnesses (e.g., epilepsy, Alzheimer's disease) leading to morbidity, disability, and stigma. Epilepsy alone affects over 50 million people worldwide, and these figures are rising as ...

EPFL2023

Hyperdimensional computing for biosignal monitoring: Applications for epilepsy detection

Una Pale

Hyperdimensional (HD) computing is a novel approach to machine learning inspired by neuroscience, which uses vectors in a hyper-dimensional space to represent data and models. This approach has gained significant interest in recent years with applications ...

EPFL2023

INCLASS: Incremental Classification Strategy for Self-Aware Epileptic Seizure Detection

David Atienza Alonso, Giovanni Ansaloni, Tomas Teijeiro Campo, Renaud Marquis

Wearable Health Companions allow the unobtrusive monitoring of patients affected by chronic conditions. In particular, by acquiring and interpreting bio-signals, they enable the detection of acute episodes in cardiac and neurological ailments. Nevertheless ...

IEEE2022

Exploration of Hyperdimensional Computing Strategies for Enhanced Learning on Epileptic Seizure Detection

David Atienza Alonso, Tomas Teijeiro Campo, Una Pale

Wearable and unobtrusive monitoring and prediction of epileptic seizures has the potential to significantly increase the life quality of patients, but is still an unreached goal due to challenges of real-time detection and wearable devices design. Hyperdim ...

2022

Personalized seizure signature: An interpretable approach to false alarm reduction for long‐term epileptic seizure detection

David Atienza Alonso, Amir Aminifar, Tomas Teijeiro Campo, Dionisije Sopic

Objective Long-term automatic detection of focal seizures remains one of the major challenges in epilepsy due to the unacceptably high number of false alarms from state-of-the-art methods. Our aim was to investigate to what extent a new patient-specific ap ...

2022

Multi-Centroid Hyperdimensional Computing Approach for Epileptic Seizure Detection

David Atienza Alonso, Tomas Teijeiro Campo, Una Pale

Long-term monitoring of patients with epilepsy presents a challenging problem from the engineering perspective of real-time detection and wearable devices design. It requires new solutions that allow continuous unobstructed monitoring and reliable detectio ...

2022

Approximate Zero-Crossing: A new interpretable, highly discriminative and low-complexity feature for EEG and iEEG seizure detection

David Atienza Alonso, Tomas Teijeiro Campo, Una Pale, Renato Zanetti

Objective: Long-term monitoring of people with epilepsy based on electroencephalography (EEG) and intracranial EEG (iEEG) has the potential to deliver key clinical information for personalised epilepsy treatment. More specifically, in outpatient settings, ...

2022

Epileptic Seizure Detection With Patient-Specific Feature and Channel Selection for Low-power Applications

Alexandre Schmid, Keyvan Farhang Razi

An accurate epileptic seizure detector using intracranial electroencephalography (iEEG) recordings, suitable for low-power wearable/implantable applications, is presented. Eleven time-domain features with low hardware complexity are employed in the feature ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Systematic Assessment of Hyperdimensional Computing for Epileptic Seizure Detection

David Atienza Alonso, Tomas Teijeiro Campo, Una Pale

Hyperdimensional computing is a promising novel paradigm for low-power embedded machine learning. It has been applied on different biomedical applications, and particularly on epileptic seizure detection. Unfortunately, due to differences in data preparati ...

2021

Interpreting deep learning models for epileptic seizure detection on EEG signals

David Atienza Alonso, Marina Zapater Sancho, Tomas Teijeiro Campo, Valentin Alexandre Guy Gabeff, Leila Cammoun

While Deep Learning (DL) is often considered the state-of-the art for Artificial Intelligence-based medical decision support, it remains sparsely implemented in clinical practice and poorly trusted by clinicians due to insufficient interpretability of neur ...

2021

Personalized Real-Time Federated Learning for Epileptic Seizure Detection

David Atienza Alonso, Amir Aminifar, Tomas Teijeiro Campo, Saleh Baghersalimi

Epilepsy is one of the most prevalent paroxystic neurological disorders. It is characterized by the occurrence of spontaneous seizures. About 1 out of 3 patients have drug-resistant epilepsy, thus their seizures cannot be controlled by medication. Automati ...

2021

Automatic detection of generalized paroxysmal fast activity in interictal EEG using time-frequency analysis

Amir Hossein Omidvarnia

Objective: Markup of generalized interictal epileptiform discharges (IEDs) on EEG is an important step in the diagnosis and characterization of epilepsy. However, manual EEG markup is a time-consuming, subjective, and the specialized task where the human r ...

2021

A Review of Microelectronic Systems and Circuit Techniques for Electrical Neural Recording Aimed at Closed-Loop Epilepsy Control

Alexandre Schmid, Reza Ranjandish

Closed-loop implantable electronics offer a new trend in therapeutic systems aimed at controlling some neurological diseases such as epilepsy. Seizures are detected and electrical stimulation applied to the brain or groups of nerves. To this aim, the signa ...

2020

Robust Epileptic Seizure Detection on Wearable Systems with Reduced False-Alarm Rate

David Atienza Alonso, Amir Aminifar, Renato Zanetti

Epilepsy affects more than 50 million people and ranks among the most common neurological diseases worldwide. Despite advances in treatment, one-third of patients still suffer from refractory epilepsy. Wearable devices for real-time patient monitoring can ...

IEEE2020

Monitoring of Cardiovascular and Neurological Diseases Using Wearable Devices

Dionisije Sopic

In this thesis, I focus on monitoring of patients suffering from cardiovascular and neurological diseases through the use of wearable devices. The main diseases considered in this thesis are atrial fibrillation (AF), myocardial infarction (MI), and epileps ...

EPFL2020

Implantable Autonomous Wireless Closed-loop Bio-electronics for Epilepsy Control

Reza Ranjandish

Implantable electronic medical device (IEMD) is an emerging technology that plays an important role in the treatment of several neurological disorders such as epilepsy, especially in the cases of drug-resistant epilepsy. Recent developments and studies sho ...

EPFL2019

A Self-Learning Methodology for Epileptic Seizure Detection with Minimally Supervised Edge Labeling

David Atienza Alonso, Amir Aminifar, Damián Pascual Ortiz

Epilepsy is one of the most common neurological disorders and affects over 65 million people worldwide. Despite the continuing advances in anti-epileptic treatments, one third of the epilepsy patients live with drug resistant seizures. Besides, the mortali ...

2019

A wearable system for real-time detection of epileptic seizures

David Atienza Alonso, Amir Aminifar, Dionisije Sopic, Renato Zanetti

A wearable system for epileptic seizure detection, comprising an eyeglasses frame, with a left arm and a right arm configured to rest over the ears of an intended person wearing the eyeglasses, a first pair of electrodes located in the left arm, and a seco ...

2019

Resource-Aware Distributed Epilepsy Monitoring Using Self-Awareness From Edge to Cloud

David Atienza Alonso, Amir Aminifar, Farnaz Forooghifar

The integration of wearable devices in humans' daily lives has grown significantly in recent years and still continues to affect different aspects of high-quality life. Thus, ensuring the reliability of the decisions becomes essential in biomedical applica ...

2019